scholarly journals Hierarchical Production Control in a Stochastic Manufacturing System with Long-Run Average Cost

1997 ◽  
Vol 214 (1) ◽  
pp. 151-172 ◽  
Author(s):  
Suresh P Sethi ◽  
Hanqin Zhang ◽  
Qing Zhang
Keyword(s):  
Average Cost ◽  
Manufacturing System ◽  
Production Control ◽  
Long Run ◽  
Hierarchical Production Control

Optimal Vacation Policy for a Manufacturing Facility Using Retrial System

2010 ◽  
Vol 447-448 ◽  
pp. 316-320
Author(s):  
Wee Meng Yeo ◽  
Xue Ming Yuan
Keyword(s):  
Average Cost ◽  
Manufacturing System ◽  
Probability Generating Function ◽  
Compound Poisson Process ◽  
System Size ◽  
Production Facility ◽  
Manufacturing Facility ◽  
Multiple Vacation ◽  
Long Run ◽  
Supplementary Variables

We consider a manufacturing system which takes vacation and subjects to breakdown. Items arrive to the production facility according to a compound Poisson process. Using supplementary variables we determine the probability generating function (p.g.f) of the system size. Under the Long Run Average Cost formulation, we show that the optimal vacation policy is either single or multiple vacation policy.

A bivariate optimal replacement policy for a repairable system

1994 ◽  
Vol 31 (4) ◽  
pp. 1123-1127 ◽  
Author(s):  
Yuan Lin Zhang
Keyword(s):  
Explicit Expression ◽  
Average Cost ◽  
Replacement Policy ◽  
Repairable System ◽  
Optimal Replacement ◽  
Long Run ◽  
Optimal Replacement Policy ◽  
Working Age ◽  
Better Than

In this paper, a repairable system consisting of one unit and a single repairman is studied. Assume that the system after repair is not as good as new. Under this assumption, a bivariate replacement policy (T, N), where T is the working age and N is the number of failures of the system is studied. The problem is to determine the optimal replacement policy (T, N)∗such that the long-run average cost per unit time is minimized. The explicit expression of the long-run average cost per unit time is derived, and the corresponding optimal replacement policy can be determined analytically or numerically. Finally, under some conditions, we show that the policy (T, N)∗ is better than policies N∗ or T∗.

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